The y-intercept of [tex]\((g/f)(x)\)[/tex]is (c) -3/2.
What is the y-intercept of the quotient function (g/f)(x)?To find the y-intercept of ((g/f)(x)), we first need to determine the expression for this quotient function.
Given the functions [tex]\(f(x) = x^2 - 6x + 8\)[/tex] and [tex]\(g(x) = x^2 - x - 12\)[/tex] , the quotient function [tex]\((g/f)(x)\)[/tex]can be written as [tex]\(\frac{g(x)}{f(x)}\).[/tex]
To find the y-intercept of ((g/f)(x)), we need to evaluate the function at (x = 0) and determine the corresponding y-value.
First, let's find the expression for ((g/f)(x)):
[tex]\((g/f)(x) = \frac{g(x)}{f(x)}\)[/tex]
[tex]\(f(x) = x^2 - 6x + 8\) and \(g(x) = x^2 - x - 12\)[/tex]
Now, let's substitute (x = 0) into (g(x)) and (f(x)) to find the y-intercept.
For [tex]\(g(x)\):[/tex]
[tex]\(g(0) = (0)^2 - (0) - 12 = -12\)[/tex]
For (f(x)):
[tex]\(f(0) = (0)^2 - 6(0) + 8 = 8\)[/tex]
Finally, we can find the y-intercept of ((g/f)(x)) by dividing the y-intercept of (g(x)) by the y-intercept of (f(x)):
[tex]\((g/f)(0) = \frac{g(0)}{f(0)} = \frac{-12}{8} = -\frac{3}{2}\)[/tex]
Therefore, the y-intercept of [tex]\((g/f)(x)\)[/tex] is [tex]\(-\frac{3}{2}\)[/tex], which corresponds to option (c).
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n simple linear regression, r 2 is the _____.
a. coefficient of determination
b. coefficient of correlation
c. estimated regression equation
d. sum of the squared residuals
The coefficient of determination is often used to evaluate the usefulness of regression models.
In simple linear regression, r2 is the coefficient of determination. In statistics, a measure of the proportion of the variance in one variable that can be explained by another variable is referred to as the coefficient of determination (R2 or r2).
The coefficient of determination, often known as the squared correlation coefficient, is a numerical value that indicates how well one variable can be predicted from another using a linear equation (regression).The coefficient of determination is always between 0 and 1, with a value of 1 indicating that 100% of the variability in one variable is due to the linear relationship between the two variables in question.
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